import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.templates.default = "plotly_white"

netflix_data = pd.read_csv(r"C:\Users\vivek\Downloads\Ml_projects\netflix_content.csv")

netflix_data.head()

	Title	Available Globally?	Release Date	Hours Viewed	Language Indicator	Content Type
0	The Night Agent: Season 1	Yes	2023-03-23	81,21,00,000	English	Show
1	Ginny & Georgia: Season 2	Yes	2023-01-05	66,51,00,000	English	Show
2	The Glory: Season 1 // 더 글로리: 시즌 1	Yes	2022-12-30	62,28,00,000	Korean	Show
3	Wednesday: Season 1	Yes	2022-11-23	50,77,00,000	English	Show
4	Queen Charlotte: A Bridgerton Story	Yes	2023-05-04	50,30,00,000	English	Movie

netflix_data['Hours Viewed'] = netflix_data['Hours Viewed'].replace(',', '', regex=True).astype(float)

netflix_data[['Title', 'Hours Viewed']].head()

	Title	Hours Viewed
0	The Night Agent: Season 1	812100000.0
1	Ginny & Georgia: Season 2	665100000.0
2	The Glory: Season 1 // 더 글로리: 시즌 1	622800000.0
3	Wednesday: Season 1	507700000.0
4	Queen Charlotte: A Bridgerton Story	503000000.0

# aggregate viewership hours by content type
content_type_viewership = netflix_data.groupby('Content Type')['Hours Viewed'].sum()

fig = go.Figure(data=[
    go.Bar(
        x=content_type_viewership.index,
        y=content_type_viewership.values,
        marker_color=['skyblue', 'salmon']
    )
])

fig.update_layout(
    title='Total Viewership Hours by Content Type (2023)',
    xaxis_title='Content Type',
    yaxis_title='Total Hours Viewed (in billions)',
    xaxis_tickangle=0,
    height=500,
    width=800
)

fig.show()

# aggregate viewership hours by language
language_viewership = netflix_data.groupby('Language Indicator')['Hours Viewed'].sum().sort_values(ascending=False)

fig = go.Figure(data=[
    go.Bar(
        x=language_viewership.index,
        y=language_viewership.values,
        marker_color='lightcoral'
    )
])

fig.update_layout(
    title='Total Viewership Hours by Language (2023)',
    xaxis_title='Language',
    yaxis_title='Total Hours Viewed (in billions)',
    xaxis_tickangle=45,
    height=600,
    width=1000
)

fig.show()

# convert the "Release Date" to a datetime format and extract the month
netflix_data['Release Date'] = pd.to_datetime(netflix_data['Release Date'])
netflix_data['Release Month'] = netflix_data['Release Date'].dt.month

# aggregate viewership hours by release month
monthly_viewership = netflix_data.groupby('Release Month')['Hours Viewed'].sum()

fig = go.Figure(data=[
    go.Scatter(
        x=monthly_viewership.index,
        y=monthly_viewership.values,
        mode='lines+markers',
        marker=dict(color='blue'),
        line=dict(color='blue')
    )
])

fig.update_layout(
    title='Total Viewership Hours by Release Month (2023)',
    xaxis_title='Month',
    yaxis_title='Total Hours Viewed (in billions)',
    xaxis=dict(
        tickmode='array',
        tickvals=list(range(1, 13)),
        ticktext=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    ),
    height=600,
    width=1000
)

fig.show()

# extract the top 5 titles based on viewership hours
top_5_titles = netflix_data.nlargest(5, 'Hours Viewed')

top_5_titles[['Title', 'Hours Viewed', 'Language Indicator', 'Content Type', 'Release Date']]

	Title	Hours Viewed	Language Indicator	Content Type	Release Date
0	The Night Agent: Season 1	812100000.0	English	Show	2023-03-23
1	Ginny & Georgia: Season 2	665100000.0	English	Show	2023-01-05
18227	King the Land: Limited Series // 킹더랜드: 리미티드 시리즈	630200000.0	Korean	Movie	2023-06-17
2	The Glory: Season 1 // 더 글로리: 시즌 1	622800000.0	Korean	Show	2022-12-30
18214	ONE PIECE: Season 1	541900000.0	English	Show	2023-08-31

# aggregate viewership hours by content type and release month
monthly_viewership_by_type = netflix_data.pivot_table(index='Release Month',
                                                      columns='Content Type',
                                                      values='Hours Viewed',
                                                      aggfunc='sum')

fig = go.Figure()

for content_type in monthly_viewership_by_type.columns:
    fig.add_trace(
        go.Scatter(
            x=monthly_viewership_by_type.index,
            y=monthly_viewership_by_type[content_type],
            mode='lines+markers',
            name=content_type
        )
    )

fig.update_layout(
    title='Viewership Trends by Content Type and Release Month (2023)',
    xaxis_title='Month',
    yaxis_title='Total Hours Viewed (in billions)',
    xaxis=dict(
        tickmode='array',
        tickvals=list(range(1, 13)),
        ticktext=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    ),
    height=600,
    width=1000,
    legend_title='Content Type'
)

fig.show()

# define seasons based on release months
def get_season(month):
    if month in [12, 1, 2]:
        return 'Winter'
    elif month in [3, 4, 5]:
        return 'Spring'
    elif month in [6, 7, 8]:
        return 'Summer'
    else:
        return 'Fall'

# apply the season categorization to the dataset
netflix_data['Release Season'] = netflix_data['Release Month'].apply(get_season)

# aggregate viewership hours by release season
seasonal_viewership = netflix_data.groupby('Release Season')['Hours Viewed'].sum()

# order the seasons as 'Winter', 'Spring', 'Summer', 'Fall'
seasons_order = ['Winter', 'Spring', 'Summer', 'Fall']
seasonal_viewership = seasonal_viewership.reindex(seasons_order)

fig = go.Figure(data=[
    go.Bar(
        x=seasonal_viewership.index,
        y=seasonal_viewership.values,
        marker_color='orange'
    )
])

fig.update_layout(
    title='Total Viewership Hours by Release Season (2023)',
    xaxis_title='Season',
    yaxis_title='Total Hours Viewed (in billions)',
    xaxis_tickangle=0,
    height=500,
    width=800,
    xaxis=dict(
        categoryorder='array',
        categoryarray=seasons_order
    )
)

fig.show()

monthly_releases = netflix_data['Release Month'].value_counts().sort_index()

monthly_viewership = netflix_data.groupby('Release Month')['Hours Viewed'].sum()

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=monthly_releases.index,
        y=monthly_releases.values,
        name='Number of Releases',
        marker_color='goldenrod', 
        opacity=0.7,
        yaxis='y1'
    )
)

fig.add_trace(
    go.Scatter(
        x=monthly_viewership.index,
        y=monthly_viewership.values,
        name='Viewership Hours',
        mode='lines+markers',
        marker=dict(color='red'),
        line=dict(color='red'),
        yaxis='y2'
    )
)

fig.update_layout(
    title='Monthly Release Patterns and Viewership Hours (2023)',
    xaxis=dict(
        title='Month',
        tickmode='array',
        tickvals=list(range(1, 13)),
        ticktext=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    ),
    yaxis=dict(
        title='Number of Releases',
        showgrid=False,
        side='left'
    ),
    yaxis2=dict(
        title='Total Hours Viewed (in billions)',
        overlaying='y',
        side='right',
        showgrid=False
    ),
    legend=dict(
        x=1.05,  
        y=1,
        orientation='v',
        xanchor='left'
    ),
    height=600,
    width=1000
)

fig.show()

netflix_data['Release Day'] = netflix_data['Release Date'].dt.day_name()

weekday_releases = netflix_data['Release Day'].value_counts().reindex(
    ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
)

# aggregate viewership hours by day of the week
weekday_viewership = netflix_data.groupby('Release Day')['Hours Viewed'].sum().reindex(
    ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=weekday_releases.index,
        y=weekday_releases.values,
        name='Number of Releases',
        marker_color='blue',
        opacity=0.6,
        yaxis='y1'
    )
)

fig.add_trace(
    go.Scatter(
        x=weekday_viewership.index,
        y=weekday_viewership.values,
        name='Viewership Hours',
        mode='lines+markers',
        marker=dict(color='red'),
        line=dict(color='red'),
        yaxis='y2'
    )
)

fig.update_layout(
    title='Weekly Release Patterns and Viewership Hours (2023)',
    xaxis=dict(
        title='Day of the Week',
        categoryorder='array',
        categoryarray=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
    ),
    yaxis=dict(
        title='Number of Releases',
        showgrid=False,
        side='left'
    ),
    yaxis2=dict(
        title='Total Hours Viewed (in billions)',
        overlaying='y',
        side='right',
        showgrid=False
    ),
    legend=dict(
        x=1.05,  
        y=1,
        orientation='v',
        xanchor='left'
    ),
    height=600,
    width=1000
)

fig.show()

# define significant holidays and events in 2023
important_dates = [
    '2023-01-01',  # new year's day
    '2023-02-14',  # valentine's ay
    '2023-07-04',  # independence day (US)
    '2023-10-31',  # halloween
    '2023-12-25'   # christmas day
]

# convert to datetime
important_dates = pd.to_datetime(important_dates)

# check for content releases close to these significant holidays (within a 3-day window)
holiday_releases = netflix_data[netflix_data['Release Date'].apply(
    lambda x: any((x - date).days in range(-3, 4) for date in important_dates)
)]

# aggregate viewership hours for releases near significant holidays
holiday_viewership = holiday_releases.groupby('Release Date')['Hours Viewed'].sum()

holiday_releases[['Title', 'Release Date', 'Hours Viewed']]

	Title	Release Date	Hours Viewed
2	The Glory: Season 1 // 더 글로리: 시즌 1	2022-12-30	622800000.0
6	La Reina del Sur: Season 3	2022-12-30	429600000.0
11	Kaleidoscope: Limited Series	2023-01-01	252500000.0
29	Perfect Match: Season 1	2023-02-14	176800000.0
124	Lady Voyeur: Limited Series // Olhar Indiscret...	2022-12-31	86000000.0
...	...	...	...
22324	The Romantics: Limited Series	2023-02-14	1000000.0
22327	Aggretsuko: Season 5 // アグレッシブ烈子: シーズン5	2023-02-16	900000.0
22966	The Lying Life of Adults: Limited Series // La...	2023-01-04	900000.0
22985	Community Squad: Season 1 // División Palermo:...	2023-02-17	800000.0
24187	Live to Lead: Limited Series	2022-12-31	400000.0

98 rows × 3 columns